Metagenomic next-generation sequencing unraveled the characteristic of lung microbiota in patients with checkpoint inhibitor pneumonitis: results from a prospective cohort study

Abstract

Background

Checkpoint inhibitor pneumonitis (CIP) is among the most lethal immune-related adverse events in patients with cancer receiving immunotherapy. This study aims to characterize the lung microbiome in patients with CIP and evaluate its diagnostic potential.

Methods

In a prospective clinical trial (NCT06192303), bronchoalveolar lavage fluid samples (BALF) were obtained from 38 patients presenting clinical symptoms and radiographic evidence of pneumonitis following immunotherapy. The cohort included 14 cases of pure-type CIP (PT-CIP), 14 cases of mixed-type CIP, and 10 cases of pulmonary infection (PI). Metagenomic next-generation sequencing (mNGS) of BALF was employed to delineate the lung microbiota profiles. Using linear discriminant analysis effect size, we discerned characteristic microbiota among the three groups and further explored the associations of signature microbiota with host immune-inflammatory markers. Functional enrichment analysis revealed potential metabolic reprogramming and differences in biological functions between patients with CIP and PI. Finally, leveraging four machine-learning models, we ascertained the clinical value of BALF microbiota profiles in diagnosing CIP.

Results

The composition of lung microbiota differed significantly between patients with CIP and PI. Microbial taxa, such as Porphyromonas, Candida, Peptostreptococcus, Treponema, and Talaromyces, exhibited distinct abundance patterns across the three groups. Correlation analysis revealed a significant positive relationship between Candida abundance and host immune-inflammatory markers, such as neutrophil-lymphocyte ratio, platelet-lymphocyte ratio, monocyte-lymphocyte ratio, and systemic immune inflammation index. In contrast, Porphyromonas demonstrated a significant negative correlation. Compared with the patients with PT-CIP, the lung microbiota of patients with PI exhibited a more diverse biological and metabolic profile. Additionally, machine learning models based on BALF microbiota profiles could accurately diagnose CIP, with the decision tree model showing the best diagnostic performance (area under the curve: 0.88).

Conclusions

Our study represents the unique characterization of the lung microbiota profiles across distinct CIP subtypes and establishes a diagnostic model for CIP based on the decision tree. These findings emphasize the value of BALF mNGS in improving the diagnosis of CIP.